P-134 INVISIBLE OFFICE WORKERS’ DISEASES
Bibliographic record
Abstract
Abstract Introduction Office workers are exposed to several risks that are often underestimated. Methods This study highlights the consequences linked to this exposure, through a retrospective study of data linked to office workers. Our investigation was carried out on a sample of 375 cases, following inclusion criteria: aged between 40 and 59 years, sedentary work in the office with exposure to the screen at least 4 hours/day. Results The average age of our study is 55.6, with a female predominance (a sex ratio of 0.41). According to the body mass index, we find 42% overweight, 39% having obesity, and only 18% have normal build. For the blood pressure, 59% had arterial hypertension, including only 32% with among them are followed by a doctor, 25% having high normal blood pressure of which 10% are followed, 8% having optimal blood pressure, and only 6.5% having normal blood pressure. Regarding visual acuity, 37% suffer from presbyopia, 9% have myopia, 35% have a combination of the two, 4% have astigmatism, and only 15% have normal visual acuity. For the diseases detected during medical visits or mentioned during the interview, we find 43% musculoskeletal disorders; 25% thyroid conditions, 21% psychological conditions and, 17% diabetes. Discussion These findings provide valuable insights into the health status of office workers, indicating a significant prevalence of various health conditions that require further medical attention and intervention. Conclusion Although the professional risks linked to office work are less visible, they aren’t absent, hence the need to apply effective preventive action in order to avoid the risks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".